The main focus of our research is to enable the intelligent and robust control of complex dynamical systems via developing a unifying framework that seamlessly fuses model-based control theory with data-driven methodologies. Our group is particularly interested in bridging the gap between rigorous physical modeling and advanced machine learning (e.g., Bayesian optimization) to enhance performance in future mobility, robotics, and advanced manufacturing. Our research leverages the interplay between control theory, data analytics, and practical industrial application.
The main focus of our research is to enable the intelligent and robust control of complex dynamical systems via developing a unifying framework that seamlessly fuses model-based control theory with data-driven methodologies. Our group is particularly interested in bridging the gap between rigorous physical modeling and advanced machine learning (e.g., Bayesian optimization) to enhance performance in future mobility, robotics, and advanced manufacturing. Our research leverages the interplay between control theory, data analytics, and practical industrial application.
